Identification Of Peptide Sequences Research Articles

Preparation, structural and morphological characterization of cartilage type II collagen peptide assemblies from sturgeon head.

Sturgeon cartilage type II collagen peptides (SHCPs) can self-assemble and be used to prepare collagen peptide assemblies. Self-assembled peptides have great potential for applications in the food industry. In the present study, self-assembled peptides were prepared from sturgeon cartilage and then characterized. The SHCPs self-assembled and formed collagen peptide assemblies. After response surface experiment optimization, the optimal enzyme digestion process comprised 43.1 °C, 3.37 h and 0.96% enzyme addition, and the peptide yield was 78.46%. Physicochemical analysis showed that the SHCPs were amphiphilic, with an average molecular weight of 1081 Da, and were rich in hydrophobic amino acids. Peptide sequence identification showed that the peptides of SHCPs with polar amino acids followed by hydrophobic amino acids could be self-assembled through hydrogen bonding and hydrophobic interaction. Through turbidity experiments, Fourier transform infrared spectroscopy and scanning electron microscopy, we demonstrated that SHCPs can self-assemble into reticular and tubular structures under specific conditions. Furthermore, both the SHCPs-Ca and SHCPs-Mg assemblies were stabilized within a pH range consistent with that of the human gastrointestinal tract. The present study provides a simple and safe method for preparing novel self-assembled peptide materials from sturgeon by-products, providing a scientific basis for the exploitation of sturgeon cartilage and potentially reducing resource wastage. © 2024 Society of Chemical Industry.

A novel antioxidant iron-chelating peptide from yak skin: analysis of the chelating mechanism and digestion stability in vitro.

The global prevalence of iron deficiency has posed significant public health risks. Animal-derived collagen peptides have been recognized for their potent metal ion-chelating capabilities, which can greatly enhance the bioavailability of iron. Yak skins, typically discarded during production and processing, serve as a valuable resource. Based on yak skin collagen peptide (YSP), we have developed a novel iron-chelating peptide: yak skin collagen iron-chelating peptide (YSP-Fe). The maximum level of iron chelation of YSP-Fe achieved was 42.72 ± 0.65 mg g-1. Structural analysis indicated that YSP-Fe was primarily formed from amino, carboxyl and carbonyl groups combined with ferrous ions. Through examination of the amino acid composition, molecular docking and peptide sequence identification, it was determined that Gly, Asp and Arg played crucial roles in the chelation of ferrous ions by YSP. Furthermore, YSP-Fe was more stable in simulated gastrointestinal digestion compared to FeSO4. YSP-Fe demonstrated dual benefits of iron supplementation and antioxidant effects. These significant findings provide a foundation for the development of novel iron supplements and the effective utilization of yak skin as a valuable resource. © 2024 Society of Chemical Industry.

Identification of Microbial Strains via 2D Cross-Correlation of LC-MS Data.

Mass spectrometry is commonly used in the identification of species present in microbial samples, but the high similarity in the peptide composition between strains of a single species has made analysis at the subspecies level challenging. Prior research in this area has employed methods such as Principal Component Analysis (PCA), the k-Nearest Neighbors' (kNN) algorithm, and Pearson correlation. Previously, 1D cross-correlation of mass spectra has been shown to be useful in the classification of small molecule compounds as well as in the identification of peptide sequences via the SEQUEST algorithm and its variants. While direct application of cross-correlation to mass spectral data has been shown to aid in the identification of many other types of compounds, this type of analysis has not been demonstrated in the literature for the purpose of LC-MS based identification of microbial strains. A method of identifying microbial strains is presented here that applies the principle of 2D cross-correlation to LC-MS data. For a set of N = 30 yeast isolate samples representing 5 yeast strains (K-97, S-33, T-58, US-05, WB-06), high-resolution LC-MS-Orbitrap data were collected. Reference spectra were then generated for each strain from the combined data of each sample of that strain. Sample strains were then predicted by computing the 2D cross-correlation of each sample against the reference spectra, followed by application of correction factors measuring the asymmetry of the 2D correlation functions.

In silico bioactivity analysis of peptide fractions derived from brewer's spent grain hydrolysates

SummaryBrewer's spent grain is an agro‐industrial waste of barley malt from the manufacture of beer, which is rich in protein. Thus, this by‐product of the beer industry could be utilised to obtain bioactive peptides through enzymatic hydrolysis. The present study aimed to evaluate potential bioactivities (anti‐inflammatory, antidiabetic, antioxidant, antimicrobial) of peptide sequences identified in 5‐kDa peptide fractions derived from brewer's spent grain hydrolysates. Two experimental hydrolysates (run 5 and run 21) were selected based on their higher degree of hydrolysis (>55%), which were subjected to peptide sequence identification and used for in silico analysis. A total of fifty peptide sequences were identified between both runs. In silico analysis identified seventeen and thirteen multifunctional peptides in runs 5 and 21, respectively, which showed between two and four potential bioactivities. Interestingly, one peptide sequence (DLPPER) showed four potential bioactivities and, according to the Docking analysis, showed better Vina scores towards the protein's targets DPP‐IV (antidiabetic) and glucocorticoid receptor (anti‐inflammatory). These results demonstrate that peptides from brewer's spent grain hydrolysates may contribute to improving human health by offering potential protection against some chronic‐related diseases; however, further follow‐up in vitro and in vivo studies are needed to evaluate its actual capability.

FEOpti-ACVP: identification of novel anti-coronavirus peptide sequences based on feature engineering and optimization.

Anti-coronavirus peptides (ACVPs) represent a relatively novel approach of inhibiting the adsorption and fusion of the virus with human cells. Several peptide-based inhibitors showed promise as potential therapeutic drug candidates. However, identifying such peptides in laboratory experiments is both costly and time consuming. Therefore, there is growing interest in using computational methods to predict ACVPs. Here, we describe a model for the prediction of ACVPs that is based on the combination of feature engineering (FE) optimization and deep representation learning. FEOpti-ACVP was pre-trained using two feature extraction frameworks. At the next step, several machine learning approaches were tested in to construct the final algorithm. The final version of FEOpti-ACVP outperformed existing methods used for ACVPs prediction and it has the potential to become a valuable tool in ACVP drug design. A user-friendly webserver of FEOpti-ACVP can be accessed at http://servers.aibiochem.net/soft/FEOpti-ACVP/.

Antibody Design for the Quantification of Photosynthetic Proteins and Their Isoforms.

Antibodies are a valuable research tool, with uses including detection and quantification of specific proteins. By using peptide fragments to raise antibodies, they can be designed to differentiate between structurally similar proteins, or to bind conserved motifs in divergent proteins. Peptide sequence selection and antibody validation are crucial to ensure reliable results from antibody-based experiments. This chapter describes the steps for the identification of peptide sequences to produce protein- or isoform-specific antibodies using recombinant technologies as well as the subsequent validation of such antibodies. The photosynthetic protein Rubisco activase is used as a case study to explain the various steps involved and key aspects to take into consideration.

Integration of Machine Learning and Structural Analysis for Predicting Peptide Antibiofilm Effects: Advancements in Drug Discovery for Biofilm-Related Infections.

The rise of antibiotic resistance has become a major concern, signaling the end of the golden age of antibiotics. Bacterial biofilms, which exhibit high resistance to antibiotics, significantly contribute to the emergence of antibiotic resistance. Therefore, there is an urgent need to discover new therapeutic agents with specific characteristics to effectively combat biofilm-related infections. Studies have shown the promising potential of peptides as antimicrobial agents. This study aimed to establish a cost-effective and streamlined computational method for predicting the antibiofilm effects of peptides. This method can assist in addressing the intricate challenge of designing peptides with strong antibiofilm properties, a task that can be both challenging and costly. A positive library, consisting of peptide sequences with antibiofilm activity exceeding 50%, was assembled, along with a negative library containing quorum-sensing peptides. For each peptide sequence, feature vectors were calculated, while considering the primary structure, the order of amino acids, their physicochemical properties, and their distributions. Multiple supervised learning algorithms were used to classify peptides with significant antibiofilm effects for subsequent experimental evaluations. The computational approach exhibited high accuracy in predicting the antibiofilm effects of peptides, with accuracy, precision, Matthew's correlation coefficient (MCC), and F1 score of 99%, 99%, 0.97, and 0.99, respectively. The performance level of this computational approach was comparable to that of previous methods. This study introduced a novel approach by combining the feature space with high antibiofilm activity. In this study, a reliable and cost-effective method was developed for predicting the antibiofilm effects of peptides using a computational approach. This approach allows for the identification of peptide sequences with substantial antibiofilm activities for further experimental investigations. Accessible source codes and raw data of this study can be found online (hiABF), providing easy access and enabling future updates.

Impact of Bioinformatics Search Parameters for Peptides' Identification and Their Post-Translational Modifications: A Case Study of Proteolysed Gelatines from Beef, Pork, and Fish.

Bioinformatics software, allowing the identification of peptides by the comparison of peptide fragmentation spectra obtained by mass spectrometry versus targeted databases or directly by de novo sequencing, is now mandatory in peptidomics/proteomics approaches. Programming the identification software requires specifying, among other things, the mass measurement accuracy of the instrument and the digestion enzyme used with the number of missed cleavages allowed. Moreover, these software algorithms are able to identify a large number of post-translational modifications (PTMs). However, peptide and PTM identifications are challenging in the agrofood field due to non-specific cleavage sites of physiological- or food-grade enzymes and the number and location of PTMs. In this study, we show the importance of customized software programming to obtain a better peptide and PTM identification rate in the agrofood field. A gelatine product and one industrial gelatine hydrolysate from three different sources (beef, pork, and fish), each digested by simulated gastrointestinal digestion, MS-grade trypsin, or both, were used to perform the comparisons. Two main points are illustrated: (i) the impact of the set-up of specific enzyme versus no specific enzyme use and (ii) the impact of a maximum of six PTMs allowed per peptide versus the standard of three. Prior knowledge of the composition of the raw proteins is an important asset for better identification of peptide sequences.

Identification of Novel Dipeptidyl Peptidase-IV Inhibitory Peptides in Chickpea Protein Hydrolysates.

Dipeptidyl peptidase-IV (DPP-IV) is one of the main targets for blood sugar control. Some food protein-derived peptides are thought to have DPP-IV inhibitory (DPP-IVi) activity. In this study, chickpea protein hydrolysates (CPHs) obtained through Neutrase hydrolysis for 60 min (CPHs-Pro-60) exhibited the highest DPP-IVi activity. DPP-IVi activity after simulated in vitro gastrointestinal digestion was maintained at >60%. Peptide libraries are established after the identification of peptide sequences. Molecular docking verified that the four screened peptides (AAWPGHPEF, LAFP, IAIPPGIPYW, and PPGIPYW) could bind to the active center of DPP-IV. Notably, IAIPPGIPYW exhibited the most potent DPP-IVi activity (half maximal inhibitory concentration (IC50): 12.43 μM). Both IAIPPGIPYW and PPGIPYW exhibited excellent DPP-IVi activity in Caco-2 cells. These results indicated that chickpea could be used as a source of natural hypoglycemic peptides for food and nutritional applications.

Silver Carp (Hypophthalmichthys molitrix) Scales Collagen Peptides (SCPs): Preparation, Whitening Activity Screening and Characterization

This study involves the preparation of scale collagen peptides (SCPs) with whitening activity from silver carp (Hypophthalmichthys molitrix) and their characterization and peptide sequence identification. In this article, scanning electron microscopy (SEM) was used to observe structure changes of sliver carp scales; enzymatic hydrolysis was optimized through protease screening and response surface optimization. The ultrafiltration was used to separate SCPs and the whitening activity was comprehensively evaluated using radical scavenging rate and tyrosinase-inhibiting activity, among others. An optimal component was characterized and identified using various modern spectral analysis techniques. The results showed that the surface of silver carp scales after decalcification was smooth and clear. The pepsin had the highest peptide yield and tyrosinase-inhibiting activity (90.01% and 82.25%, respectively). The optimal enzymatic hydrolysis conditions were an enzyme dosage of 16.1%, a solid-liquid ratio of 1:15.6 and a time of 4.9 h. The proportions of hydrophobic and basic amino acids in the peptide composition were 32.15% and 13.12%, respectively. Compared with SCPs2, SCPs1 (6096.68-9513.70 Da) showed better ·OH scavenging ability, tyrosinase-inhibiting activity and moisture absorption. SCPs1 was a macromolecular fragment of type I collagen with a triple helix structure, containing three peptide sequences with the potential for tyrosinase activity inhibition (AGPPGADGQTGQRGE, SGPAGIAGPAGPRGPAGPNGPPGKD and KRGSTGEQGSTGPLGMRGPRGAA). These results show that SCPs1 is a collagen peptide product with whitening potential.

Immunoanalytical Approach for Detecting and Identifying Ancestral Peptide Biomarkers in Early Earth Analogue Environments.

Several mass spectrometry and spectroscopic techniques have been used in the search for molecular biomarkers on Mars. A major constraint is their capability to detect and identify large and complex compounds such as peptides or other biopolymers. Multiplex immunoassays can detect these compounds, but antibodies must be produced for a large number of sequence-dependent molecular targets. Ancestral Sequence Reconstruction (ASR) followed by protein "resurrection" in the lab can help to narrow the selection of targets. Herein, we propose an immunoanalytical method to identify ancient and universally conserved protein/peptide sequences as targets for identifying ancestral biomarkers in nature. We have developed, tested, and validated this approach by producing antibodies to eight previously described ancestral resurrected proteins (three β-lactamases, three thioredoxins, one Elongation Factor Tu, and one RuBisCO, all of them theoretically dated as Precambrian), and used them as a proxy to search for any potential feature of them that could be present in current natural environments. By fluorescent sandwich microarray immunoassays (FSMI), we have detected positive immunoreactions with antibodies to the oldest β-lactamase and thioredoxin proteins (ca. 4 Ga) in samples from a hydrothermal environment. Fine epitope mapping and inhibitory immunoassays allowed the identification of well-conserved epitope peptide sequences that resulted from ASR and were present in the sample. We corroborated these results by metagenomic sequencing and found several genes encoding analogue proteins with significant matches to the peptide epitopes identified with the antibodies. The results demonstrated that peptides inferred from ASR studies have true counterpart analogues in Nature, which validates and strengthens the well-known ASR/protein resurrection technique and our immunoanalytical approach for investigating ancient environments and metabolisms on Earth and elsewhere.

Inter-Regional Proteomic Profiling of the Human Brain Using an Optimized Protein Extraction Method from Formalin-Fixed Tissue to Identify Signaling Pathways.

Proteomics offers vast potential for studying the molecular regulation of the human brain. Formalin fixation is a common method for preserving human tissue; however, it presents challenges for proteomic analysis. In this study, we compared the efficiency of two different protein-extraction buffers on three post-mortem, formalin-fixed human brains. Equal amounts of extracted proteins were subjected to in-gel tryptic digestion and LC-MS/MS. Protein, peptide sequence, and peptide group identifications; protein abundance; and gene ontology pathways were analyzed. Protein extraction was superior using lysis buffer containing tris(hydroxymethyl)aminomethane hydrochloride, sodium dodecyl sulfate, sodium deoxycholate, and Triton X-100 (TrisHCl, SDS, SDC, Triton X-100), which was then used for inter-regional analysis. Pre-frontal, motor, temporal, and occipital cortex tissues were analyzed by label free quantification (LFQ) proteomics, Ingenuity Pathway Analysis and PANTHERdb. Inter-regional analysis revealed differential enrichment of proteins. We found similarly activated cellular signaling pathways in different brain regions, suggesting commonalities in the molecular regulation of neuroanatomically-linked brain functions. Overall, we developed an optimized, robust, and efficient method for protein extraction from formalin-fixed human brain tissue for in-depth LFQ proteomics. We also demonstrate herein that this method is suitable for rapid and routine analysis to uncover molecular signaling pathways in the human brain.

Identification of novel peptide sequences to fuse to iduronate-2-sulfatase for the treatment of Hunter syndrome

Identification of novel peptide sequences to fuse to iduronate-2-sulfatase for the treatment of Hunter syndrome

Abstract 1697: Identification of Peptide Sequence Served as Genetically-Encoded or Chemical Probes to Inhibit Gαs in Cells

Abstract 1697: Identification of Peptide Sequence Served as Genetically-Encoded or Chemical Probes to Inhibit Gαs in Cells

Production and identification of dipeptidyl peptidase IV (DPP-IV) inhibitory peptides from discarded cowhide collagen

The present study focused on the effects of different preparation conditions on the activity and peptide sequence identification of DPP-IV inhibitory peptides based on discarded cowhide collagen and its regulation mechanism. The highest DPP-IV inhibitory activity (IC50 of 3.04 ± 0.13 mg/mL) was obtained by the combination of double enzymatic hydrolysis (papain and compound protease) and optimized IL-US (ultrasound-assisted ionic liquids) pretreatment. The optimal pretreatment condition for ultrasonic power, ultrasonic time, ionic liquid/collagen ratio, and collagen concentration were 389 W, 25 min, 0.7, and 0.3 %, respectively. IL-US pretreatment induced the exposure of active sites and hydrophobic amino acids of collagen peptide, promoted the partial transformation of β-sheet to β-turn, and enhanced the stability of gastrointestinal digestion. Seven new peptides with DPP-IV inhibitory ability were identified. Importantly, further molecular docking showed that five peptides (GPVG, FGPGP, APGGAP, GPPGPT, and GPVGPPG) were proposed to be antidiabetic peptides from cowhide collagens.

Valorization of leftover green tea residues through conversion to bioactive peptides using probiotics-aided anaerobic digestion.

Bioactive peptides (BPs) are protein fragments that benefit human health. To assess whether leftover green tea residues (GTRs) can serve as a resource for new BPs, we performed in silico proteolysis of GTRs using the BIOPEP database, revealing a wide range of BPs embedded in GTRs. Comparative genomics and the percentage of conserved protein analyses enabled us to select a few probiotic strains for GTR hydrolysis. The selected probiotics digested GTRs anaerobically to yield GTR-derived peptide fractions. To examine whether green tea (GT) peptide fractions could be potential mediators of host-microbe interactions, we comprehensively screened agonistic and antagonistic activities of 168 human G protein-coupled receptors (GPCRs). NanoLC-MS/MS analysis and thin-layer chromatography allowed the identification of peptide sequences and the composition of glycan moieties in the GTRs. Remarkably, GT peptide fractions produced by Lactiplantibacillus plantarum APsulloc 331261, a strain isolated from GT, showed a potent-binding activity for P2RY6, a GPCR involved in intestinal homeostasis. Therefore, this study suggests the potential use of probiotics-aided GTR hydrolysates as postbiotic BPs, providing a biological process for recycling GTRs from agro-waste into renewable resources as health-promoting BPs.

MStoCIRC: A powerful tool for downstream analysis of MS/MS data to predict translatable circRNAs.

CircRNAs are formed by a non-canonical splicing method and appear circular in nature. CircRNAs are widely distributed in organisms and have the features of time- and tissue-specific expressions. CircRNAs have attracted increasing interest from scientists because of their non-negligible effects on the growth and development of organisms. The translation capability of circRNAs is a novel and valuable direction in the functional research of circRNAs. To explore the translation potential of circRNAs, some progress has been made in both experimental identification and computational prediction. For computational prediction, both CircCode and CircPro are ribosome profiling-based software applications for predicting translatable circRNAs, and the online databases riboCIRC and TransCirc analyze as many pieces of evidence as possible and list the predicted translatable circRNAs of high confidence. Simultaneously, mass spectrometry in proteomics is often recognized as an efficient method to support the identification of protein and peptide sequences from diverse complex templates. However, few applications fully utilize mass spectrometry to predict translatable circRNAs. Therefore, this research aims to build up a scientific analysis pipeline with two salient features: 1) it starts with the data analysis of raw tandem mass spectrometry data; and 2) it also incorporates other translation evidence such as IRES. The pipeline has been packaged into an analysis tool called mass spectrometry to translatable circRNAs (MStoCIRC). MStoCIRC is mainly implemented by Python3 language programming and could be downloaded from GitHub (https://github.com/QUMU00/mstocirc-master). The tool contains a main program and several small, independent function modules, making it more multifunctional. MStoCIRC can process data efficiently and has obtained hundreds of translatable circRNAs in humans and Arabidopsis thaliana.

The flax genome reveals orbitide diversity

BackgroundRibosomally-synthesized cyclic peptides are widely found in plants and exhibit useful bioactivities for humans. The identification of cyclic peptide sequences and their precursor proteins is facilitated by the growing number of sequenced genomes. While previous research largely focused on the chemical diversity of these peptides across various species, there is little attention to a broader range of potential peptides that are not chemically identified.ResultsA pioneering study was initiated to explore the genetic diversity of linusorbs, a group of cyclic peptides uniquely occurring in cultivated flax (Linum usitatissimum). Phylogenetic analysis clustered the 5 known linusorb precursor proteins into two clades and one singleton. Preliminary tBLASTn search of the published flax genome using the whole protein sequence as query could only retrieve its homologues within the same clade. This limitation was overcome using a profile-based mining strategy. After genome reannotation, a hidden Markov Model (HMM)-based approach identified 58 repeats homologous to the linusorb-embedded repeats in 8 novel proteins, implying that they share common ancestry with the linusorb-embedded repeats. Subsequently, we developed a customized profile composed of a random linusorb-like domain (LLD) flanked by 5 conserved sites and used it for string search of the proteome, which extracted 281 LLD-containing repeats (LLDRs) in 25 proteins. Comparative analysis of different repeat categories suggested that the 5 conserved flanking sites among the non-homologous repeats have undergone convergent evolution driven by functional selection.ConclusionsThe profile-based mining approach is suitable for analyzing repetitive sequences. The 25 LLDR proteins identified herein represent the potential diversity of cyclic peptides within the flax genome and lay a foundation for further studies on the functions and evolution of these protein tandem repeats.

ACE-inhibitory activities of peptide fractions (

In this study, the effects of five adjunct lactobacilli on the formation of angiotensin-converting enzyme inhibitory (ACE-i) peptides in model cheeses were investigated. A total of seven peptide fractions (less than 3 kDa) were obtained by means of a fraction collector coupled with a HPLC system. ACE-i activity and peptide sequence analysis of the seven fractions were determined by RP-HPLC and then MALDI-ToF-MS techniques, respectively. Peptides produced from αs1- and κ-caseins had higher amounts compared to those produced by other caseins. There were considerable differences between the fractions in terms of peptide sequences and ACE-i activities. In general, the peptide fraction containing Lactobacillus delbrueckii ssp. bulgaricus exhibited a higher level of ACE-i activity (79.56%) than the other samples. The next highest ACE-i activity was determined in the cheese containing L. plantarum and significant differences were observed between the ACE-i activities of the fractions. The highest ACE-i activity was identified in fractions including f1 (51.39%) for L. casei, f6 (33.49%) for L. helveticus, f4 (46.62%) for L. helveticus DPC4571 and f1 (47.96%) for cheese containing non-adjunct culture. In conclusion, use of lactobacilli as an adjunct in model cheese increased the peptide types and amounts and also ACE-i activity.

Pattern enrichment analysis for phage selection of stapled peptide ligands.

Phage display is the most widely used technique to discover de novo peptides that bind to target proteins. However, it is associated with some challenges such as compositional bias. In this study, to overcome these difficulties, we devised a 'pattern enrichment analysis.' In this method, two samples (one obtained by affinity selection, the other simply amplified without selection) are prepared, and the two sequence datasets read on next-generation sequencer are compared to find the three-residue pattern most enriched in the selected sample. This allows us to compare two sequence datasets with high coverage and facilitates the identification of peptide sequences and the key residues for binding. We also demonstrated that this approach in the combination with structured peptide libraries allowed spatial mapping of the enriched sequence patterns. Here, we prepared a phage library displaying chemically stapled helical peptides with the X1C2X3X4X5X6X7X8C9X10 sequence, where X is any amino acid. To validate our method, we performed screening against the HDM2 protein. The results showed that the hydrophobic residues (Phe, Tyr, Trp and Leu) that are key to interactions with HDM2 were clearly identified by the pattern enrichment analysis. We also performed selection targeting the SARS-CoV-2 spike RBD in the same manner. The results showed that similar patterns were enriched among the hit peptides that inhibited the protein-protein interaction.